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基于大型语言和图像识别模型的2型糖尿病管理的人工智能营养师:临床前概念验证研究
Haonan Sun1, Kai Zhang1, Wei Lan2
1School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
Journal of medical Internet research
|November 9, 2023
概括
一个人工智能营养师计划显示出对中国糖尿病管理的希望,提供个性化的饮食建议. 人工智能 (AI) 系统成功通过了营养师考试,并准确识别了食品成分,准备进行试点测试.
科学领域:
- 医疗保健中的人工智能
- 营养科学 营养科学
- 糖尿病管理 糖尿病管理
背景情况:
- 中国的糖尿病营养管理面临挑战,原因是注册临床营养师短缺.
- 一个基于人工智能 (AI) 的营养学计划被开发来解决这个差距.
- 该程序使用先进的语言和图像识别来识别食和饮食建议.
研究的目的:
- 评估支持新型营养学家计划的人工智能模型的能力和有效性.
- 评估AI在为2型糖尿病患者提供基于证据的饮食建议方面的能力.
- 为了验证人工智能驱动的食品成分识别的准确性.
主要方法:
- 对T2DM患者和内分泌学家进行了调查,以确定饮食知识上的差距.
- 对中国注册营养师考试进行了大型语言模型 (ChatGPT,GPT 4.0) 的测试.
- 将人工智能生成的营养建议与专家饮食师的建议进行比较,并评估用于食品识别的图像识别模型.
主要成果:
- 大多数患者 (88.4%) 希望立即获得营养支持.
- 聊天GPT和GPT 4.0都成功通过了营养学家的检查.
- 人工智能图像识别模型 (Dino V2) 在成分识别方面获得了0.825的高F1得分,人工智能饮食建议在很大程度上与专家建议保持一致.
结论:
- 人工智能模型在T2DM中展示了支持营养管理的有希望的能力.
- 人工智能营养学家计划经过验证,并为监督试点研究做好了准备.
- 开发的AI系统有可能通过可访问的营养指导来增强糖尿病护理.
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